Download Forecasting Financial Markets Using Neural Networks: An by Jason E. Kutsurelis PDF

By Jason E. Kutsurelis

This study examines and analyzes using neural networks as a forecasting instrument. in particular a neural network's skill to foretell destiny traits of inventory marketplace Indices is verified. Accuracy is in comparison opposed to a conventional forecasting strategy, a number of linear regression research. eventually, the chance of the model's forecast being right is calculated utilizing conditional possibilities. whereas basically in brief discussing neural community conception, this learn determines the feasibility and practicality of utilizing neural networks as a forecasting software for the person investor. This research builds upon the paintings performed by means of Edward Gately in his e-book Neural Networks for monetary Forecasting. This learn validates the paintings of Gately and describes the improvement of a neural community that accomplished a 93.3 percentage likelihood of predicting a marketplace upward thrust, and an 88.07 percentage chance of predicting a marketplace drop within the S&P500. It was once concluded that neural networks do have the aptitude to forecast monetary markets and, if accurately educated, the person investor may gain advantage from using this forecasting device.

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Extra info for Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy

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Homoscedasticity assumes variation or error around the regression line should be similar for low and high values of the independent variable. This can be verified by examining the residual plots for each independent variable. For each variable, there did not seem to be major differences in the variability of the residual for different values of the independent variable. Therefore, the Homoscedasticity assumption was valid. Autocorrelation, or the likelihood that a certain type of error precedes or follows another type of error, violates the independence of errors assumption.

Katsuaki Terasawa of the Naval Postgraduate School. First, the historical data was calculated within Excel from the raw data during the period from March 3, 1991 to 30 August 18, 1998. An if-then statement was used to identify a percentage rise. The statement generates a one if the percent change is greater than zero or a zero if the percent change is less than zero. Summing this column provides the total days with a percent increase. This was subtracted from the total number of days in the data set to identify market falls.

Multiple linear regression model Multiple linear regression analysis was performed using the Data Analysis tool within Excel. The output of this process for the final model is presented in Table 7. Table 7. 19588E-16 Transportation Index C Dow Utilities Index C Amex Oil Index C Amex Oil Index C Excel was also used to calculate the Variance Inflationary Factor (VIF). The results are given below in Table 8. 48 Table 8. 11 Dow Transportation R Square Index C Dow Utilities Index C Index C The Normal probability plot, Line Fit Plots, and Residual Plots for the Multiple Regression Model are provided in Appendix A.

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